Microfluidic cell sorters have shown great potential to revolutionize the current technique of enriching rare cells. In the past decades, different microfluidic cell sorters have been developed by researchers for separating circulating tumor cells, T-cells, and other biological markers from blood samples. However, it typically takes months or even years to design these microfluidic cell sorters by hand. Thus, researchers tend to use computer simulation (usually finite element analysis) to verify their designs before fabrication and experimental testing. Despite this, conducting precision finite element analysis of microfluidic devices is computationally expensive and labor-intensive. To address this issue, we recently presented a microfluidic simulation method that can simulate the behavior of fluids and particles in some typical microfluidic chips instantaneously. Our method decomposes the chip into channels and intersections. The behavior of fluid in each channel is determined by leveraging analogies with electronic circuits, and the behavior of fluid and particles in each intersection is determined by querying a database containing 92,934 pre-simulated channel intersections. While this approach successfully predicts the behavior of complex microfluidic chips in a fraction of the time required by existing techniques, we nonetheless identified three major limitations with this method: (1) the library of pre-simulated channel intersections is unnecessarily large (only 2,072 of 92,934 were used); (2) the library contains only cross-shaped intersections (and no other intersection geometries); and (3) the range of fluid flow rates in the library is limited to 0 to 2 cm/s. To address these deficiencies, in this work we present an improved method for instantaneously simulating the trajectories of particles in microfluidic chips. Firstly, inspired by dynamic programming, our new method optimizes the generation of pre-simulated intersection units and avoids generating unnecessary simulations. Secondly, we constructed a cloud database (http://cloud.microfluidics.cc) to share our pre-simulated results and to let users become contributors and upload their simulation results into the cloud database as a benefit to the whole microfluidic simulation community. Lastly, we investigated the impact of different channel angles and different fluid flow rates on predicting the trajectories of particles. We found a wide range of device geometries and flow rates over which our existing simulation results can be extended without having to perform additional simulations. Our method should accelerate the simulation of particles in microfluidic chips and enable researchers to design new microfluidic cell sorter chips more efficiently.
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The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review
Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs.
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- NSF-PAR ID:
- 10445596
- Date Published:
- Journal Name:
- Biosensors
- Volume:
- 13
- Issue:
- 3
- ISSN:
- 2079-6374
- Page Range / eLocation ID:
- 389
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Schwenker, Friedhelm (Ed.)Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/bios13030389
